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Reinforcement Learning

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/pwc-1/Paper-9/tree/main/7/rand_conv-master

Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning

https://github.com/pytorch/captum

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/pytorch/rl/tree/main/examples/a2c

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/pytorch/rl/tree/main/examples/cql

Conservative Q-Learning for Offline Reinforcement Learning

https://github.com/pytorch/rl/tree/main/examples/decision_transformer

Decision Transformer: Reinforcement Learning via Sequence Modeling

https://github.com/pytorch/rl/tree/main/examples/dqn

Playing Atari with Deep Reinforcement Learning

https://github.com/pytorch/rl/tree/main/examples/dreamer

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/pytorch/rl/tree/main/examples/iql

Offline Reinforcement Learning with Implicit Q-Learning

https://github.com/pytorch/rl/tree/main/examples/ppo

Proximal Policy Optimization Algorithms

https://github.com/pytorch/rl/tree/main/examples/sac

Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor

https://github.com/pytorch/rl/tree/main/examples/td3

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/q275212/rlcard

RLCard: A Toolkit for Reinforcement Learning in Card Games

https://github.com/qbetterk/parlai

ParlAI: A Dialog Research Software Platform

https://github.com/qdevpsi3/adaptive-policy-iteration

Adaptive Approximate Policy Iteration

https://github.com/qdevpsi3/quantum-arch-search

Quantum Architecture Search via Deep Reinforcement Learning

https://github.com/qdevpsi3/randomized-value-iteration

Generalization and Exploration via Randomized Value Functions

https://github.com/qgallouedec/deep_rl

A Distributional Perspective on Reinforcement Learning

https://github.com/qgallouedec/lge

First return, then explore

https://github.com/qi-pang/mdpfuzz

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

https://github.com/qiankun214/DQN-FlappyBird-python3

Playing Atari with Deep Reinforcement Learning

https://github.com/qihongl/demo-advantage-actor-critic

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/qihongl/demo-random-search-RL

Simple random search provides a competitive approach to reinforcement learning

https://github.com/qihongl/dlstm-demo

Asynchronous Methods for Deep Reinforcement Learning

https://github.com/qingyangzhang/empo

TTRL: Test-Time Reinforcement Learning

https://github.com/qingzew/tensorflow-ram

Recurrent Models of Visual Attention

https://github.com/qingzwang/DiverseImageCaptioning

Towards Diverse and Accurate Image Captions via Reinforcing Determinantal Point Process

https://github.com/qingzwang/DiversityMetrics

Describing like humans: on diversity in image captioning

https://github.com/qinwang/leela-zero

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

https://github.com/qmaai/open_spiel

OpenSpiel: A Framework for Reinforcement Learning in Games

https://github.com/qq303067814/DQLearning-Toolbox

A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem

https://github.com/qq456cvb/multi-stage-detection

Multi-Stage Reinforcement Learning For Object Detection

https://github.com/quantaji/AMD-SSD

Multi-agent Reinforcement Learning in Sequential Social Dilemmas

https://github.com/quantum-fusion/AI-deeplearning

Self-critical Sequence Training for Image Captioning

https://github.com/quantumiracle/Popular-RL-Algorithms

Memory-based control with recurrent neural networks

https://github.com/quanvuong/handful-of-trials-pytorch

Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models

https://github.com/quasimetric-learning/torch-quasimetric

An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.